Interactive location queries for raw machine data

ABSTRACT

A data intake and query system may store raw machine data that includes location information. A client system may include a user interface for searching the data intake and query system. The user interface allows a user to define a field search query and to define one or more ad-hoc boundary regions on a map. A combined query is transmitted to the data intake and query system, the combined query including both the field search query and location search information that is based on the ad-hoc boundary regions. The data intake and query system runs the combined query and returns responsive results, which are displayed at the client user interface.

BACKGROUND

Information is often classified based on location. Such information maybe collected and classified in an intentional manner, with location dataand related data stored in a database. Data that is associated with thelocation information may then be searched based on location. Forexample, a large business with multiple locations may associateinformation such as sales or inventory to a particular store location.That may then be searched by store location such that only informationfor selected store locations is returned in response to the search.Similarly, information may be classified based on geographic locations,including jurisdictional boundaries such as states and cities.

Because such systems only allow certain types of information to beassociated with location, only those same limited types of informationare searchable by location. Accordingly, such a system may be limited asto the types of information that can be searched, resulting in a limitedfunctionality that is difficult to remedy after the information hasalready been associated with a location and stored. In addition, thetype of location information of the data set may itself be limited, forexample, to particular categories such as a store location orjurisdictional boundaries. Thus, the data set may have limited utilityoutside of its intended application.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 depicts exemplary steps for searching geographic data inaccordance with some embodiments of the present disclosure;

FIGS. 19A-19C depict an exemplary user interface in accordance with someembodiments of the present disclosure;

FIG. 20 depicts an exemplary choropleth geographic visualization inaccordance with some embodiments of the present disclosure;

FIG. 21 depicts an exemplary time-series geographic visualization inaccordance with some embodiments of the present disclosure;

FIG. 22 depicts exemplary steps for generating a combined query inaccordance with some embodiments of the present disclosure;

FIG. 23 depicts exemplary steps for processing of a combined query by adata query system in accordance with some embodiments of the presentdisclosure; and

FIG. 24 depicts exemplary steps for processing and displaying queryresponses in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

A data intake and query system may receive data that includes locationinformation such as geographic information. This information may beprovided to the data intake and query system within raw machine datareceived from a variety of disparate data sources and stored as events.The location information from the disparate data sources may also beprovided in a variety of different formats within the events, which mayidentify location information in different ways. As an example, someevents may include location information as coordinates or locationrelative to a reference point, while some events may include informationthat may be used to derive a location, such as a name of a town, IPaddresses, and a variety of other information types.

An interactive user interface may provide a user with the ability tosearch events from disparate data sources based on both locationinformation and queries of other fields. A search string may begenerated for searching non-location fields of the events, based on userinput and selections. An interactive location portion of the userinterface may display an interactive map. A variety of tools may beprovided that allow the user to generate an overlay for the map, whichdefines ad-hoc boundary regions. Information about the ad-hoc boundaryregions is combined with the search string to provide a combined query.

The data intake and query system receives and processes the combinedquery. Responsive results are generated from the raw machine data basedon both the search string and the locations that fall within the ad-hocboundary regions. All of the results must meet the requirements of thesearch sting and the fall within the ad-hoc boundary region. Exemplaryresults may be events, values from events, and analyses of events andvalues. The results are returned to the user interface system, whichdisplays results and information about results, such as statistics andvisualizations. The visualizations may be provided for the interactivemap, which may include a choropleth map depicting information such asthe number of results for each ad-hoc boundary region.

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the FIGURE) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIGURE) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in FIGURE) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. This statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes.

There is also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the ]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.1. Location Intake and Data Query System

As described herein, various types of data may be stored and searchableat an intake and data query system. In some embodiments, the data mayinclude location data such as geographic data. Although the presentdisclosure may generally discuss exemplary embodiments involvinggeographic data (e.g., data that may be associated with pre-definedcoordinate system), the present disclosure may also be utilized withlocations that are identified or associated with other referencesystems. As is described herein, raw machine data may include varioustypes of information that may be related to location. Such informationmay be converted to a common reference system (such as a coordinatesystem) or other reference systems. For example, in an environment suchas a school, factory, mall, retail store, amusement park, or stadium,raw machine data may include information relating to beacons or wirelesssignals (e.g., WiFi signals), which may be used to identify a locationassociated with the raw machine data relative to a reference location inthe environment. Accordingly, while the present disclosure may discussgeographic information, it will be understood that the systems andmethods herein may be applied to any suitable location information.

Location data may include any suitable values that may be used to deriveinformation about a location associated with an event. As with othertypes of data described herein, location data may be provided in rawmachine data that is acquired and stored by the intake system, and maybe searchable (e.g., using a late-binding schema) by the data querysystem. Location data may include any suitable values that provide alocation or that may be used to derive a location, such as GeographicCoordinate System coordinates, Universal Transverse Mercator (UTM)coordinates, IP addresses, addresses, zip codes, municipalities, namesof geographic features (e.g., lakes, rivers, mountains, creeks, etc.),cell tower identifiers, WiFi hotspot identifiers, beacon signals, or anyother suitable information from which a location may be derived.

A data query system may be used to search events for the location. Insome embodiments, a variety of different types of location data havingdifferent formats may be identified and converted to a common referencesystem for purposes of search, statistics, reports, visualizations, andany other suitable analysis. For example, a data query system searchinggeographic data may identify a variety of different types of values thatinclude coordinates (e.g., latitude and longitude, or UTM coordinates)and other geographic data that can be associated with coordinates, andconvert the different geographic data types into a common geographicsystem such as longitude and latitude for the Geographic CoordinateSystem. In this manner, disparate types of geographic data may besearched and analyzed based on this common reference system.

In some embodiments, it may be desired to search events for a particularlocation or for locations within a physical region such as a geographicregion. Events may be queried for locations that match a requestedlocation or fall within a requested region. Although events may besearched in any suitable manner, in one embodiment a point-in-polygontechnique may be used to determine whether values (e.g., derived ordetermined coordinates) for location data of events correspond to aphysical region or geographic region. A polygon may be associated with aregion to be searched, with the interior region of the polygon definingthe locations values that are responsive to the search. In someembodiments, the point-in-polygon technique may be performed inaccordance with U.S. patent application Ser. No. 14/815,022, entitled“EFFICIENT POLYGON-CLIPPING TECHNIQUE TO REDUCE DATA TRANSFERREQUIREMENTS FOR A VIEWPORT”, filed on 31 Jul. 2015, application Ser.No. 14/606,396, entitled “EFFICIENT POINT-IN-POLYGON INDEXING TECHNIQUEFOR PROCESSING QUERIES OVER GEOGRAPHIC DATA SETS”, filed on 27 Jan.2015, application Ser. No. 14/606,387, entitled “EFFICIENTPOINT-IN-POLYGON TECHNIQUE TO FACILITATE DISPLAYING GEOGRAPHIC DATA,”filed on 27 Jan. 2015, and application Ser. No. 14/700,685, entitled“THREE DIMENSIONAL POINT-IN-POLYGON OPERATION TO FACILITATE DISPLAYINGTHREE-DIMENSIONAL STRUCTURES,” filed on 27 Jan. 2015, each of which ishereby incorporated by reference in its entirety for all purposes.

FIG. 18 depicts exemplary steps for searching geographic data inaccordance with some embodiments of the present disclosure. Although thesteps of FIG. 18 are generally described in the context of geographicdata, it will be understood that they apply equally to other data suchas any suitable location data. For example, location search informationmay describe any suitable reference to location. Geographic searchinformation may refer to a type of location search information that isspecific to searching for geographic information (e.g., coordinates).The steps depicted by FIG. 18 are provided for illustrative purposesonly; those skilled in the art will understand that additional steps maybe included, that or more steps may be removed, and that the ordering ofthe steps of FIG. 18 may be modified in any suitable manner. It will beunderstood that while particular hardware, software, system components,geographic data types, and search techniques may be described in thecontext of FIG. 18, that the steps described herein are not so limited.

At step 1802, the data query system may receive a request to searchevents for geographic data. The request may include search informationsuch as geographic search information that defines criteria forsearching geographic data. In some embodiments, geographic searchinformation may include any suitable criteria such as coordinates,information that may be used to derive coordinates, one or morepreviously defined geographic regions, any other suitable geographiccriteria, or any combination thereof. In some embodiments, thegeographic criteria may be provided within a unique JavaScript ObjectNotation for transmitting geographic information to the intake and querysystem (e.g., a Geo JSON). The request to search events for geographicdata may also be a combined query that includes other search criteriasuch as a field search query, which may include any suitable fields ofevents to be searched as described herein. However, it will beunderstood that geographic search information may be provided in aseparate query than the field search query, with the two queries beingrelated so as to provide a single set of results. In one embodiment,timing criteria may be provided to specify time-sensitive or real-timemonitoring, for example, based on a query that limits returned values tovalues that have occurred within a requested or recent time period. Oncethe request to search events for geographic data has been received,processing may continue to step 1804.

At step 1804, the data query system may generate the geographic searchbased on geographic search information. In some embodiments, generatingthe geographic search may comprise establishing coordinates for use by apoint-in-polygon system. For example, in embodiments where thegeographic search information includes information used to derivecoordinates, the coordinates may be derived based on the information.Exemplary embodiments include deriving coordinates based on warehousefacilities, retail locations, transportation facilities, entertainmentvenues, transportation infrastructure, natural or artificialtransportation arteries, municipal boundaries, any other suitableinformation, or any combination thereof. In some embodiments, predefinedgeographic regions may have an identifier and may be associated withknown coordinates, such that the coordinates may be accessed based onthe identifier being provided in the request to search geographic data.For example, in some embodiments, lookup definitions may be provided ina lookup table, and may be accessible based on criteria such as anidentifier. The lookup definition may include the identifier and otherinformation related to a predefined geographic region, such as a fileincluding coordinates associated with the predefined geographic region.In some embodiments, the coordinates may be available from one or morekeyhole markup language (KML) files, which may be stored within akeyhole markup language zipped (KMZ) file, or from an ERSI Shapefile(SHP). Once the geographic search is generated, processing may continueto step 1806.

At step 1806, the data query system may search events based on querysuch as a combined query that includes the geographic search informationand the field search query. As described herein, the data query systemmay search events including raw machine data and metadata from a varietyof heterogeneous data sources. In some embodiments, some stored eventsmay include information that may be converted to a common referencesystem with the geographic search information (e.g., coordinates such aslatitude and longitude) to be searched, as described herein. Forexample, information such as IP addresses, WiFi identifiers, cell towerlocations, geographic features, or any other suitable information may beused to derive data (e.g., coordinates) for the common reference system.The coordinates for values of events may then be searched in anysuitable manner, such as by using a point-in-polygon methodology. If afield search query is provided, the data query system may also searchbased on the field search query, limiting responsive values to thosevalues that correspond to the both the geographic search information andthe field search query. In some embodiments, a query having geographicsearch information may be provided separately from the field searchquery. Either query may be run first, with the other query searching theresults of the first query. Whichever methods are used for searchingbased on queries with geographic search information, processing may thencontinue to step 1808.

At step 1808, the data query system may perform any requested analysisbased on the values that are responsive to the search of step 1806. Insome embodiments, a search request may also include a request to provideanalysis of events, for example, for use in visualizations, reports,statistics, and other analysis, as described herein. Exemplary analysesmay include counts (e.g., of events or values) for geographic regions,comparisons between different regions (e.g., choropleth maps),statistical analyses of values within geographic regions (e.g., averageexpenditures, etc.), other analyses described herein, and anycombination thereof. Any suitable portion of the analysis may beperformed at the data query system. In some embodiments, the data querysystem may analyze the responsive values and perform a completeanalysis. In one embodiment, the data query system may search for thevalues that are responsive to the geographic search information andfield search query, and generate the requested visualization based onthose responsive values and associated time stamps. For example, a dataquery system may receive a request that includes search criteria for acompany's vehicles and for a particular location, and a request togenerate a visualization of how many of those vehicles entered thatlocation during different time periods. In other embodiments, anysuitable portion of this analysis may be performed by the data querysystem. Once any requested analysis has been performed, processing maycontinue to step 1810.

At step 1810, the data query system may return the results to theclient. As described at steps 1802-1808, the results are responsive tothe geographic search information and the field search query, and mayinclude values, analysis, or any suitable combination thereof. Theresponsive results may be provided to the client, and the processing ofthe request may end.

3.2. User Interface for a Location Query System

FIG. 19A depicts an exemplary user interface 1900 in accordance withsome embodiments of the present disclosure. Although the user interfaceof FIG. 19A may be employed at any suitable location (e.g., client orserver, remote or on-site) and using any suitable device (e.g., acomputer, tablet, smart phone, etc.), in one embodiment the userinterface of FIG. 19A may comprise a client user interface and may beemployed on a display of a computer operating at a remote location fromthe data query system. The user interface of FIG. 19A may allow a userto generate combined queries including both location search informationand a field search query, defining the location search information basedon the creation of one or more ad-hoc boundary regions. Ad-hoc boundaryregions are regions that may be modified by a user in a manner that isnot confined to jurisdictional boundaries (e.g., national, state,municipality, county, etc.) or geographic boundaries (e.g., lakes,rivers, oceans, mountain ranges, etc.). Ad-hoc boundary regions may becreated based on jurisdictional boundaries and geographic boundaries, aslong as the result is not confined to jurisdictional boundaries orgeographic boundaries. For example, an ad-hoc boundary region could becreated from a jurisdictional boundary that is then expanded in a manner(e.g., drawing an irregular shape expanding into a neighboringjurisdiction) that results in a region that is no longer confined tojurisdictional boundaries. The combined query may be provided to a dataquery system for processing and responsive values and analysis may bereceived from the data query system, as described herein.

In the embodiment depicted in FIG. 19A, the location information to besearched may be geographic information. However, it will be understoodthat the user interface of FIG. 19A may be used for searching of anylocation information, instead of or in combination with the geographicinformation. Although a client user interface may include any suitableuser interface elements, in some embodiments the user interface 1900 mayinclude a query input portion 1910, an interactive geographic portion1930, and a results display portion 1950. Although these user interfaceelements may be displayed in a particular arrangement and configurationin FIG. 19A, it will be understood that the user interface elements maybe rearranged in any suitable manner, and that one or more of thedisplay elements may be displayed separately (e.g., as a pop-up window)or in any other suitable manner (e.g., displaying results on a separatescreen or window after running a search).

Referring to FIG. 19B, query input portion 1910 may comprise elementsthat facilitate the creation and running of search queries for data suchas raw machine data that may be searched via a data query system.Although it will be understood that query input portion 1910 may includeany suitable components, in an exemplary embodiment query input portionmay include a search bar 1912, selection interfaces 1914, time rangepicker 1916, preexisting queries 1918, analysis selection 1920, testselection 1922, store selection 1924, and submit selection 1926.

Search bar 1912 may accept user input in the form of a search string(e.g., a search string written in SPL), which may define one or moreextraction rules (e.g., based on a regular expression defined by auser), as described herein. In some embodiments, a portion of the searchstring may be automatically created or updated based on other inputs,for example, inputs to the interactive geographic portion 1930,selection interfaces 1914, and preexisting queries 1918. In someembodiments, the search bar 1912 may be utilized to then further modifythe portion of the search string that was automatically created orupdated. In this manner, a user may interactively create search strings,access preexisting search strings, and modify automatically createdsearch strings, as part of creating a search string to be submitted tothe data query system as the field search query.

A selection interface 1914 may allow for the automatic creation ofportions of a field search query based on user selections. The selectioninterface 1914 may be applied to any suitable field or combination offields, and may include any suitable interface, such as pull down menus,text input boxes, numerical selection interfaces, and binary selections.A selection within the selection interface may result in an appropriatesearch string being automatically created and added to a field searchquery or search bar 1912. For example, in one embodiment a customselection interface 1914 may allow for selection of carriers thatprovide shipments to a business. Selection of one or more carriers mayresult in an appropriate search string being automatically createdwithin search bar 1912, after which a user may modify the newly createdsearch string.

A time range picker 1916 may allow for the selection of a time range. Insome embodiments, the time range picker 1916 may also result in aselection of a real-time time range, in which search results are limitedto the most recent results. In some embodiments, a definition of realtime (e.g., only current results, results within the last 30 seconds,one minute, etc.) may be set as a default, or may be selected within thetime range picker 1916. A selection within the selection interface mayresult in an appropriate search string being added to a field searchquery or search bar 1912. For example, in an exemplary embodiment ofcarriers that provide shipments to a business, selection of real timeanalysis within time range picker 1916 may result in the automaticgeneration of an appropriate search string within the field searchquery, such that only real time values corresponding to carriers thatprovide shipments to the business are returned by the data query system.

Preexisting queries 1918 may allow for the selection of queries thatalready exist, such as query templates or commonly-used queries thathave been saved by a user. Any suitable interface for preexistingqueries 1918 may be provided in accordance with the present disclosure,such as pull-down menus, search interfaces, and browsing interfaces. Insome embodiments, the client system may access preexisting queries fromanother device over a network. As described herein, in some embodimentsa location identifier (e.g., a geographic identifier) may correspond toa definition of an ad-hoc boundary region stored at the intake and querysystem, and the preexisting queries 1918 may be based on the locationidentifiers. A selection of the preexisting query may result in anappropriate search string (e.g., including the location identifier)being added to a field search query or search bar 1912. For example, inone embodiment of carriers that provide shipments to a business, aplurality of search string templates may be provided to selectparticular carriers or products, and upon selection of the template, thesearch bar 1912 may be automatically populated with the search stringtemplate.

An analysis selection interface 1920 may facilitate the selection ofanalyses to be performed by the client system and data query system. Anysuitable interface for analysis selection 1920 may be provided inaccordance with the present disclosure, such as pull-down menus, searchinterfaces, and browsing interfaces. In some embodiments, the clientsystem may have the ability to access different types of analyses fromover a network, for example, from a server. An analysis selection mayresult include a selection of statistics, time series data,visualizations, or any other suitable analysis or representation ofvalues accessible from events stored at the data query system. One ormore of these analyses may then be depicted within the interactivegeographic portion 1930 and results display portion 1950 as describedherein. A selection within the analysis selection interface 1920 mayresult in an appropriate search string being added to a field searchquery or search bar 1912. In some embodiments, a user may then modifythe analysis string within the search bar 1912.

Test selection 1922 may facilitate the testing of search queries. Insome embodiments, it may be desirable to test a search query, forexample, by running a search query against a subset of data. Testselection may allow for a user to run such a test query, and may be anysuitable interface (e.g., a button, menu selection, etc.) located withinany suitable portion of the interactive display (e.g., within the queryinput portion or results display portion). Selecting the test selectionmay result in providing a test query to test data (e.g., recent datareceived at the client or a subset of data available from a data querysystem). For example, in one embodiment, selection of test selection1922 may result in the current query (e.g., the search string of thecurrent query box and any other selections as described herein) beingtransmitted to the data query system, which may return values or events.In some embodiments, an event may be returned that has values associatedwith the current search query emphasized in some manner (e.g.,highlighting, bold, italics, etc.). These results may be displayed, forexample, as the results display output in the results display portion1950. In some embodiments, while the test selection 1922 remainsselected, changes in the search string or other search criteria may bedynamically changed in the events, and in some embodiments, the searchstring may be modified based on selections of examples and counterexamples within the displayed results.

Store selection 1924 may allow a user to select a search query for lateruse. Store selection 1924 may be implemented in any suitable manner,such as a button or pull down menu, and in some embodiments, may provideoptions for storing different aspects of a search query, such as asearch string or one or more selections from query input portion 1910.When store selection 1924 is selected, the selected aspects of thesearch query may be made available for future use, e.g., as preexistingqueries 1918.

Submit selection 1926 may allow a user to submit a query to a data querysystem. The submit selection 1926 may include any suitable selectionmechanism such as a button, a menu, a pull-down menu, or a keyboardstroke (e.g., a carriage return). In response to the submit selection1926 a field search query may be created based on the search string ofthe input bar 1912 and in some embodiments, other inputs and selectionsof query input portion. In some embodiments, geographic searchinformation may also be generated in response to the submit selection1926, for example, based on interactive geographic input in theinteractive geographic portion 1930. In some embodiments, a combinedquery may be generated based on the field search query and thegeographic search information. The combined query may be generated inany suitable manner, such as by appending a geographic search queryincluding the geographic search information to the field search query,or inserting the geographic information (e.g., identifiers, coordinates,etc.) into the field search query. The combined query may then beprovided to the data query system for processing.

Referring to FIG. 19C, interactive geographic portion 1930 may compriseelements that facilitate the creation of ad-hoc boundary regions basedon a map input provided by a user, and the generation of geographicsearch information for searching geographic regions within a data querysystem. By defining ad-hoc regions and generating geographic searchinformation, events having values that correspond to the field searchquery may be further filtered based on the geographic searchinformation. Although the present discussion of user interface 1900 isprovided in the context of an interactive geographic portion, it will beunderstood that an interactive map portion may be provided in a similarmanner, and may be applicable to any type of location information (e.g.,locations within a school, factory, mall, retail store, amusement park,or stadium), to generate ad-hoc boundary regions for use in generatinglocation search information for the intake and query system. Forexample, ad-hoc boundary regions may be defined based on relativedistances to a reference point at a location.

Although it will be understood that interactive geographic portion 1930may include any suitable components to facilitate the user providing themap input, in an exemplary embodiment interactive geographic portion mayinclude map portion 1932, user-defined overlay 1934, map view portion1936, geographic tool selection 1938, preexisting ad-hoc boundary regionselection 1940, and region storage selection 1942.

Map portion 1932 may include a display of a map for use in thedefinition of ad-hoc boundary regions. Although it will be understoodthat any suitable map may be depicted within map portion 1932 inaccordance with the present disclosure, in some embodiments map portionmay include a two-dimensional map of a geographic region. Although atwo-dimensional black-and-white map may be depicted in FIGS. 19A and19C, it will be understood that the map may provide any suitabledepiction of the geographic region, including images or otherrepresentations of the geographic region. The map may include otherinformation such as jurisdictional information, labels of for geographicfeatures, topographical information, depth information, or map-specificinformation such as the presence of forests, wildlife population, arableland, human population, population density, energy usage, or any othersuitable information that may be displayed on a map. In someembodiments, any of this information may be selectable by a user, forexample, by selecting a region, a label associated with a region orinformation depicted on the map, definition of criteria for map-specificinformation depicted on a map, or any other suitable selection method.

User-defined overlay 1934 may depict ad-hoc boundary regions overlayingthe map portion 1932. In some embodiments, a plurality of user-definedregions may define a single ad-hoc boundary region, based on the user'sdefinition of that region. Although it will be understood thatuser-defined overlay 1934 may depict the ad-hoc boundary regions in anysuitable manner, in some embodiments the ad-hoc boundary regions may bepartially transparent such that the content of the underlying map isvisible through the ad-hoc boundary regions. In some embodiments,depictions of ad-hoc boundary regions may include degrees of color,shading, contrast, opacity, and other suitable visual depictions thatallow ad-hoc boundary regions to be distinguished from the underlyingmap and from each other. In some embodiments, the depictions may beindividually modifiable for each ad-hoc boundary region, and depictionsmay change, for example, based on a number of responsive values withinrespective ad-hoc boundary regions or a relative proportion ofresponsive values within a respective ad-hoc boundary region.

Map view portion 1936 may provide an interface that allows a user tomodify the current map view. Although map view portion 1936 is depictedas a simple box in FIG. 19C, it will be understood that varied tools maybe provided for modifying a current map view at various locations withinthe interactive geographic portion 1930. For example, a dragging tool(e.g., a hand depicted over the map portion 1932) may permit a user todrag the map. Zooming interfaces (e.g., zoom buttons or slidingselectors) may change the zoom of the map. Depicted information (e.g.,map-specific information such as population density or arable land) maybe changed based on a selection from a menu or other interface. Thegeographic region represented by the map may be changed (e.g., bychanging a selection of a depicted jurisdictions). In this manner, mapview portion 1936 may allow a user to modify not only the portion of themap that is visible but the type of information that is visible, and insome embodiments, may be used as a basis for creation of an ad-hocboundary region.

Geographic tool selection 1938 may provide an interface for selectionfrom a variety of tools for creating ad-hoc boundary regions. It will beunderstood that geographic tool selection 1938 may use any suitable userinterface techniques for receiving selection of a tool for creatingad-hoc boundary regions, such as drop down menus or selectable icons.Although any suitable tools may be selected in accordance with thepresent disclosure, in an embodiment the tools may include a free-formdrawing tool, a point-to-point drawing tool, a center point and radiusdrawing tool, a geographic feature selection tool, and a map-specificinformation selection tool.

An exemplary free-form drawing tool may allow a user to drag the tool tocreate a region using an interface such as a mouse, a touch-screen, or astylus. The region may track the path that the user selects, creating acompleted ad-hoc boundary region once the path results an enclosure of aregion, with the enclosed region corresponding to the ad-hoc boundaryregion. In some embodiments, if the path created by the user does notcreate an enclosed region, one or more options for an enclosed regionmay be created based on the path. In the case of more than one potentialenclosed regions, the user may be permitted to view a preview and selectfrom the available regions.

A point-to-point drawing tool may allow a user to define a set ofpoints, with lines between the points resulting in the creation of anenclosed region that defines the ad-hoc boundary region. In an exemplaryembodiment, a user may select a series of points in sequence. For eachset of consecutive points, a line may be drawn between the points. Whenthe final point corresponds to initial point, or in some embodiments,when the lines created by the set of points create an enclosed region,the ad-hoc boundary regions may be completed. In some embodiments, auser may make a selection to create an enclosed region based on pointsand lines that do not yet form an enclosed region, in which case theenclosed region defining the ad-hoc boundary region may be createdautomatically. In some embodiments, the connecting line between twoconsecutive points need not be a straight line, and may be created inother paths (e.g., arcs). In some embodiments, the lines defining thead-hoc boundary region may be modifiable after the ad-hoc boundaryregion is initially created.

A center point and radius drawing tool may allow a user to create acircle by selecting or defining a center point for a circle and defininga radius or diameter for the circle. The completed circle may define anad-hoc boundary region. The selection of the center point may beperformed in any suitable manner, such as by pointing to a center point(e.g., with a mouse, touch screen, or stylus), defining coordinates fora center point, or defining a location for the center point. Theselection of the radius or diameter may also be performed in anysuitable manner, such as by pointing to or dragging a point located atthe circumference of the circle or typing a value for the radius (e.g.,in miles).

A shape creation tool may allow for the creation of other shapes such assquares, rectangles, triangles, polygons, ovals, etc. A shape creationtool may use any suitable user interface (e.g., selection mechanismssuch as a mouse, touch screen, stylus, text input, menus, or anysuitable combination thereof) for creation of a tool. In someembodiments, icons may be dragged and dropped, and shapes may bemanipulated (e.g., expanded, rotated, combined, etc.). The resultingshape may define an ad-hoc boundary region.

A geographic feature selection tool may allow any suitable geographicfeature such as rivers, mountains, bays, canyons, and other features tobe selected. Geographic features may be selected in any suitable manner,such as by physically selecting a feature (e.g., clicking on orselecting a geographic feature with a mouse, touch screen or stylus),searching for a feature (e.g., via a search engine), or selecting fromavailable features within the map area (e.g., from a menu of selectablefeatures). The enclosed region created by the geographic selection toolmay define the ad-hoc boundary region. In some embodiments, once ageographic feature is selected and depicted within the user-definedoverlay, the shape may be modified by a user.

As described above, in some embodiments a depicted map may includemap-specific information such as such as the presence of forests,wildlife population, arable land, human population, population density,energy usage, or any other suitable information. In some embodiments,this information may be used to determine one or more ad-hoc boundaryregions. Although map-specific information may be selected in anysuitable manner, in some embodiments the selection mechanism may dependon the type of map specific information. For example, certain types ofmap specific information such as forests or arable land may definecontiguous areas that may be selected by a user. Other types ofmap-specific information may be selected based on a criteria, such asareas have a population density greater than a threshold. The geographicregions defined by such map-specific information may define one or moread-hoc boundary regions.

Whatever tool or combination of tools is used to create an ad-hocboundary region, the ad-hoc boundary region is used to generategeographic search information that is associated with the locations andscale depicted in the underlying map. Although those boundaries may bedefined in any suitable manner, in some embodiments a set of coordinatesmay be determined that correspond to the exterior boundary of the ad-hocboundary region. It will also be understood that other geographic searchinformation may be determined, for example, based on a combination ofcoordinates and line and shape definitions.

Preexisting ad-hoc boundary region selection 1840 may facilitate a userselecting one or more preexisting ad-hoc boundary regions for display onthe user-defined overlay of the map display of the geographic region.Although a preexisting ad-hoc boundary region may originate from avariety of sources, in some embodiments the preexisting ad-hoc boundaryregions may be user-generated, accessed from the intake and data querysystem, or provided from a remote library accessible over a network.Preexisting ad-hoc boundary region selection 1840 may include anysuitable user interface for selecting from among available preexistingad-hoc boundary regions, such as menus and search engines. A pluralityof preexisting ad-hoc boundary regions may be selected at any one time.In some embodiments, a plurality of preexisting ad-hoc boundary regionsmay be previewed to a user, for example, by using different colors forthe user-defined overlay for different preexisting ad-hoc boundaryregions.

Region storage selection 1842 may allow a user to select a depictedad-hoc boundary region for storage for later use. Although depicted as asimple box in FIG. 18C, it will be understood that any suitable userinterface mechanism may be utilized for regions storage selection 1842.In some embodiments, multiple regions may be combined into a singlead-hoc boundary region. A plurality of ad-hoc boundary regions may bestored at one time. The user interface of the region storage selection1842 may permit the user to modify and enter information about an ad-hocboundary region, such as a name, identifier, description, or any othersuitable information.

Results display portion 1850 may provide for the display of results thatare received from the intake and query system, in response to thecombined query. Although it will be understood that any suitable resultsmay be displayed in the results display portion, examples of results mayinclude responsive value, responsive events, statistics, visualizations,analyses, or any suitable combination thereof. In some embodiments, auser may be able to select between different results to be displayedwithin the results display portion. Moreover, the results display andorder of results may be modified based on a user selection (e.g.,chronological order, alphabetical order, numerical value, etc.).

In some embodiments, the interactive geographic portion 1830 may displaygeographic visualizations, such as real-time depictions of the number ofvalues and events (e.g., a count, a choropleth display, etc.) that areresponsive to the search query, and analyses of results. Results displayportion 1850 may include results (e.g., values or events) thatcorrespond to a region of the map (e.g., a selected ad-hoc boundaryregion).

FIG. 20 depicts an exemplary choropleth geographic visualization inaccordance with some embodiments of the present disclosure. AlthoughFIG. 20 is depicted in the context of a geographic visualization, itwill be understood that a similar visualization may be equally appliedto any other sort of location information, such as locations within aschool, factory, mall, retail store, amusement park, or stadium. It willbe understood that the exemplary depiction of FIG. 20 includes only aportion of the display, and that certain display elements have beenexcluded for the sake of clarity. The choropleth geographicvisualization includes a map region 2002, first ad-hoc boundary region2004, second ad-hoc boundary region 2006, third ad-hoc boundary region2008, selection interface 2010, and geofencing interface 2012. Depictedbelow the choropleth map is a results display 2014.

Map region 2002 may be modifiable and selectable, as described herein.As the map region 2002 is changed to include a different geographicregion, the selection interface 2010 may also be updated to includedifferent ad-hoc boundary regions that are available for selection. Inthe exemplary embodiment of FIG. 20, four ad-hoc boundary regions areselectable within selection interface 2010, but the only three regionsdisplayed are those associated with first ad-hoc boundary region 2004,second ad-hoc boundary region 2006, and third ad-hoc boundary region2008, based on the selection within selection interface 2010.

As is depicted by the different shading of the first, second, and thirdad-hoc boundary regions in FIG. 20, the choropleth display may providefor a visual depiction of the number of responses (e.g., events orvalues) corresponding to each ad-hoc boundary region (e.g., asdetermined by a intake and query system utilizing a point-in-polygontechnique. Additional information may be displayed for each ad-hocboundary region, such as a count of the number of responses. In someembodiments, the displayed results may be modified based on a userselection of one of the ad-hoc boundary regions. For example, based onthe selection of the second ad-hoc boundary region 2006, results for thesecond ad-hoc boundary region 2006 may be displayed.

As described herein, a time range picker may allow for the selection ofresults for any suitable time frame, including real-time results. Insome embodiments, the time range picker may be integrated with theinteractive geographic portion of the display. In an exemplaryembodiment of real-time results, the choropleth display of the ad-hocboundary regions may update dynamically, such that the display of eachof the regions changes with the volume or real-time data.

In some embodiments, a user may also configure notifications based on ageofencing interface 2012. Although a geofencing interface 2012 maydisplay any suitable information, in some embodiments it may provideselectable thresholds, ranges, values, or any combination thereof, forselection of criteria for providing a notification. For example, if anumber of responses within an ad-hoc boundary region exceeds athreshold, a notification may be provided. Similarly, certain values maybe flagged as requiring a notification. The notification may also beselectable, and includes any suitable notification such as an update toany portion of the display, a sound notification, an electronicnotification (e.g., text or e-mail), logging of data to a data storagesystem, invoking of software and hardware to perform a task, any othersuitable notification, or any combination thereof.

FIG. 21 depicts an exemplary time-series geographic visualization inaccordance with some embodiments of the present disclosure. AlthoughFIG. 21 is depicted in the context of a geographic visualization, itwill be understood that such a similar visualization may be equallyapplied to any other sort of location information, such as locationswithin a school, factory, mall, retail store, amusement park, orstadium. The time-series geographic visualization includes a map region2102, first ad-hoc boundary region 2104, second ad-hoc boundary region2106, third ad-hoc boundary region 2108, selection interface 2110,geofencing interface 2112, and time-series selection interface 2116.Depicted below the choropleth map is a results display 2114. Althoughthe time-series geographic visualization may function in any suitablemanner, in one embodiment the components of the time-series geographicvisualization may function in a similar manner to the choroplethgeographic visualization of FIG. 20.

Time-series selection interface 2116 may provide a visual display of theoccurrence of events on a time scale, and may allow for the selection ofa range of events to display within the geographic visualization and theresults display 2114. Although time-series selection interface maydepict the visual display of occurrences in any suitable manner, in oneembodiment bars may represent the number of results corresponding to aparticular time range. In some embodiments, the interface may provide aseparate display for each of the ad-hoc boundary regions. In otherembodiments, the display may be combined by stacking color-coded barsassociated with each of the ad-hoc boundary regions, as is depicted inFIG. 21. Although a time range may be selected in any suitable manner,in an embodiment the time range may be selected by selecting two barsthat define the time range.

FIG. 22 depicts exemplary steps for generating a combined query inaccordance with some embodiments of the present disclosure. The stepsdepicted by FIG. 22 are provided for illustrative purposes only; thoseskilled in the art will understand that additional steps may beincluded, that or more steps may be removed, and that the ordering ofthe steps of FIG. 22 may be modified in any suitable manner. It will beunderstood that while particular hardware, software, system components,geographic data types, and search techniques may be described in thecontext of FIG. 22, that the steps described herein are not so limited.Although the steps of FIG. 22 are provided in the context of ageographic search, it will be understood that similar steps aresimilarly applicable to any other location information, such aslocations within a school, factory, mall, retail store, amusement park,or stadium.

At step 2202, a device such as a client device may receive a definitionof an ad-hoc boundary region. As described herein, there are a varietyof methods by which a user may define an ad-hoc boundary region (e.g.,based on a user selection within an interactive geographic portion1830). A plurality of non-contiguous regions may define one ad-hocboundary region, and a plurality of ad-hoc boundary regions may bedefined at the same time. The ad-hoc boundary region may be depicted asa user-defined overlay for a geographic region that is depicted withinthe interactive geographic portion. Once the ad-hoc boundary region hasbeen defined, processing may continue to step 2204.

At step 2204, the device such as the client device may generate and sendgeographic search information (e.g., coordinates defining an ad-hocboundary region, sent within a specific Geo JSON) to the intake and dataquery system. The geographic search information is generated based onthe ad-hoc boundary regions, and may include information suchcoordinates that define the ad-hoc boundary region. The message may besent at any suitable time, such as after a user provides a request toprovide the ad-hoc boundary region to the data query system, orasynchronously whenever an ad-hoc boundary region is added or modified.In some embodiments, the geographic search information may be sent alongwith a request for information, such as an identifier for the ad-hocboundary region being submitted to the data query system. Once thegeographic search information is sent, processing may continue to step2206.

At step 2206, the device such as the client device may receive anidentifier that may be used to identify the ad-hoc boundary region. Inthis manner, the client device can quickly and easily request a searchfor events within the ad-hoc boundary region based on the reference. Insome embodiments, the reference (e.g., a name or identifier) may bestored along with a definition of the ad-hoc boundary region, for lateruse (e.g., for providing preexisting ad-hoc boundary region selection1840). Once the identifier has been received, processing may continue tostep 2208.

At step 2208, the device such as the client device may generate thefield search query, for example, based on a regular expression andselections made within the query input portion 1810, as well as otherinputs. The field search query may provide information for conducting aquery for values within events, as well as parameters for the data querysystem to perform analysis and provide visualizations. Once the fieldsearch query is generated, processing may continue to step 2210.

At step 2210, the device such as the client device may generate acombined message based on the field search query and the geographicsearch information or identifier. Although the combined message may bedescribed as a single message, it will be understood that a combinedmessage may include a group of messages that are related such that asearch can be performed that satisfies both the field search query andthe geographic search information. Once the combined message has beengenerated, the combined message may be transmitted to the data querysystem for processing. The steps of FIG. 22 may then end.

FIG. 23 depicts exemplary steps for processing of a combined query by adata query system in accordance with some embodiments of the presentdisclosure. The steps depicted by FIG. 23 are provided for illustrativepurposes only; those skilled in the art will understand that additionalsteps may be included, that or more steps may be removed, and that theordering of the steps of FIG. 23 may be modified in any suitable manner.It will be understood that while particular hardware, software, systemcomponents, geographic data types, and search techniques may bedescribed in the context of FIG. 23, that the steps described herein arenot so limited. Although the steps of FIG. 23 are provided in thecontext of a geographic search, it will be understood that similar stepsare similarly applicable to any other location information, such aslocations within a school, factory, mall, retail store, amusement park,or stadium.

At step 2302, the data query system may receive and process the combinedquery. The data query system may determine searches to perform based onthe field search query, identify requested statistics and analyses,identify on or more ad-hoc boundary areas to be searched (e.g., based onan identifier provided as the geographic search information), or prepareto perform any other suitable analyses or operations. Processing maythen continue to step 2304.

At step 2304, the data query system may identify responsive events. Asdescribed herein, the data query system may utilize a technique such aspoint-in-polygon technique to search for events that occur within thead-hoc boundary, while utilizing event searching to identify events thatmeet the field search query (e.g., including requested fields andmeeting time range criteria). The search results may be limited toevents that include fields and values that are responsive to both thegeographic search information and the field search query. In someembodiments, a search may first be performed for events that are locatedwithin the ad-hoc boundary region, and the resulting records may besearched based on the field search query. In some embodiments, a searchmay first be performed for events that are responsive to the fieldsearch query, and the resulting records may be searched to determinewhether they are located within the ad-hoc boundary region. In someembodiments, both types of searches may be performed in parallel andevents satisfying both searches may be identified as responsive events.Once the responsive events have been identified, processing may continueto step 2306.

At step 2306, the data query system may generate the query responses.Although generating query responses may be performed in any suitablemanner, in some embodiments the events, values, analysis,visualizations, or any combination thereof may be generated based on thecombined query. The query responses may be associated with informationsuch as a timestamp and an identifier for the ad-hoc boundary regionthat corresponds to each query response. Processing may then continue tostep 2308, at which the data query system may transmit the queryresponses to the client. Processing of the steps of FIG. 23 may thenend.

FIG. 24 depicts exemplary steps for processing and displaying the queryresponses in accordance with some embodiments of the present disclosure.The steps depicted by FIG. 24 are provided for illustrative purposesonly; those skilled in the art will understand that additional steps maybe included, that or more steps may be removed, and that the ordering ofthe steps of FIG. 24 may be modified in any suitable manner. It will beunderstood that while particular hardware, software, system components,geographic data types, and search techniques may be described in thecontext of FIG. 24, that the steps described herein are not so limited.Although the steps of FIG. 24 are provided in the context of ageographic search, it will be understood that similar steps aresimilarly applicable to any other location information, such aslocations within a school, factory, mall, retail store, amusement park,or stadium.

At step 2402, the device such as the client may receive the queryresponses for updating the display. In some embodiments receiving thequery responses may include extracting events, values, metadata,analysis results, and visualizations, timestamps, and geographicidentifiers from the query responses. In some embodiments, some or allof the analysis and generation of visualizations may occur at the client(e.g., based on received values, associated timestamps, and geographicidentifiers). Processing may then continue to step 2404.

At step 2404, the interactive geographic display (e.g., interactivegeographic portion) of the display may be updated based on the receivedquery responses. For example, a choropleth display may be updated basedon the number of events falling within each ad-hoc boundary region, andthe value of a numeric indicator may also be updated. Display elementssuch as a time-series of a time-series playback display may be updated.Processing may then continue to step 2406.

At step 2406, the results display (e.g., results display portion) may beupdated to provide a results display output. As described herein, anysuitable results (e.g., events, values, analyses, visualizations, etc.)may be displayed as the results display output. In some embodiments,results may be displayed separately for each ad-hoc boundary region, andthe display of results may be changed based on any suitable field,metadata, values, statistics, or other information, or any combinationthereof. Once the results display is updated, processing may continue tostep 2408.

At step 2408, the display may be updated based on user inputs. Asdescribed herein, a user may be able to make selections that may modifythe display of results. In some embodiments, it may also be possible tolocally perform additional querying, analysis, and visualizations on thereceived results, i.e., without querying the data query system. Anychanges to the data to be displayed as a result of such user inputs willbe provided as updates of the display. The processing of the steps ofFIG. 24 may then end.

The foregoing provides illustrative examples of the present disclosure,which are not presented for purposes of limitation. It will beunderstood by a person having ordinary skill in the art that variousmodifications may be made by within the scope of the present disclosure.It will also be understood that the present disclosure need not take thespecific form explicitly described herein, and the present disclosure isintended to include variations to and modifications thereof, consistentwith the appended claims. It will also be understood that variations ofthe systems, apparatuses, and processes may be made to further optimizethose systems, apparatuses, and processes. The disclosed subject matteris not limited to any single embodiment described herein, but rathershould be construed in breadth and scope in accordance with the appendedclaims.

What is claimed is:
 1. A computer-implemented method for providing auser interface for an interactive data query, comprising: receiving aquery input at a user interface, wherein the query input defines a fieldsearch query for searching raw machine data; receiving a map input at aninteractive map portion of the user interface, wherein the interactivemap portion displays a map region; displaying, at the user interface, auser-defined overlay of the map region based on the map input, whereinthe user-defined overlay depicts one or more ad-hoc boundary regions;generating location search information for searching raw machine databased on the one or more ad-hoc boundary regions; generating a combinedquery based on the field search query and the location searchinformation; transmitting the combined query to a data query system;receiving a set of results from the data query system, wherein thereceived results are responsive to both the field search query and thelocation search information, and wherein individual results within theset of results identify a value from the raw machine data and atimestamp corresponding to the value; displaying a visualization of theset of results as an ordering of the set of results over a time range,wherein the ordering is determined based at least in part on thetimestamps identified within the results, and wherein a portion of thevisualization is selectable to indicate a subrange of the time range;obtaining a selection of the portion of the visualization; anddisplaying a subset, of the set of results, that identify a timestampwithin the subrange.
 2. The computer-implemented method of claim 1,wherein the map input is received based on or more of a free-formdrawing tool, a point-to-point drawing tool, a center point and radiusdrawing tool, a shape creation tool, a geographic feature selectiontool, and a map-specific information selection tool.
 3. Thecomputer-implemented method of claim 1, further comprising: receivingone or more location identifiers from the data query system, whereineach location identifier corresponds to the location search informationand is associated with a lookup definition for the one or more ad-hocboundary regions; and storing the location identifiers.
 4. Thecomputer-implemented method of claim 1 further comprising: receiving oneor more location identifiers from the data query system, wherein eachlocation identifier corresponds to the location search information andis associated with a lookup definition for the one or more ad-hocboundary regions; and storing the location identifiers; displaying aselection interface for the location identifiers; receiving a selectionof one or more of the location identifiers; updating the user-definedoverlay of the map region based on the selection; receiving a second mapinput at the interactive map portion; and identifying a new ad-hocboundary region based on the updated user-defined overlay and the secondmap input.
 5. The computer-implemented method of claim 1, furthercomprising: receiving one or more location identifiers from the dataquery system, wherein each location identifier corresponds to thelocation search information and is associated with a lookup definitionfor the one or more ad-hoc boundary regions; and storing the locationidentifiers; accessing a plurality of previously stored locationidentifiers, displaying a selection interface for the received locationidentifiers and the preexisting location identifiers; receiving aselection of one or more of the received location identifiers and one ormore of the preexisting location identifiers; updating the user-definedoverlay of the map region based on the selection; and identifying a newad-hoc boundary region based on the updated user-defined overlay.
 6. Thecomputer-implemented method of claim 1, further comprising updating aresults display based on the received results.
 7. Thecomputer-implemented method of claim 1, wherein the received resultsinclude a plurality of values and a plurality of timestamps, whereineach timestamp is associated with a value, wherein each value is aportion of an event, and wherein each event comprises raw machine data.8. The computer-implemented method of claim 1, further comprising:identifying, based on the timestamps, a subset of the values thatcorrespond to real-time data; and displaying the subset of the valuesthat correspond to real-time data.
 9. The computer-implemented method ofclaim 1, further comprising: identifying, based on the timestamps,whether a threshold number of the values correspond to real-time data;and generating a notification when the threshold number of the valuescorresponds to real-time data.
 10. The computer-implemented method ofclaim 1, wherein receiving the set of results from the data query systemcomprises continuously receiving the set of results while the fieldsearch query and location search information are unchanged, and whereindisplaying the visualization comprises continuously updating thevisualization according to the continuously-received received results.11. The computer-implemented method of claim 1, wherein the one or moread-hoc boundary regions comprise a plurality of ad-hoc boundary regions,wherein an intersecting region corresponds a region where the pluralityof ad-hoc boundary regions overlap, and wherein the location searchinformation is based on the intersecting region.
 12. Thecomputer-implemented method of claim 1, wherein the map input comprisesa geographic input, wherein the interactive map portion comprises aninteractive geographic map, wherein the map region comprises ageographic region, and wherein the location search information comprisesgeographic search information.
 13. The computer-implemented method ofclaim 1, wherein the map input comprises a geographic input, wherein theinteractive map portion comprises an interactive geographic map, whereinthe map region comprises a geographic region, and wherein the locationsearch information comprises geographic search information, and whereinthe geographic search information comprises coordinates that define thead-hoc boundary region.
 14. The computer-implemented method of claim 1,wherein the map input comprises a geographic input, wherein theinteractive map portion comprises an interactive geographic map, whereinthe map region comprises a geographic region, and wherein the locationsearch information comprises geographic search information, wherein atleast a portion of the raw machine data is in different format than thegeographic search information, and wherein the portion of the rawmachine data is converted to a common reference system for searching ofthe raw machine data.
 15. The computer-implemented method of claim 1,wherein the map region comprises one or more of a school, factory, mall,retail store, amusement park, or stadium.
 16. The computer-implementedmethod of claim 1, wherein the map region comprises one or more of aschool, factory, mall, retail store, amusement park, or stadium, andwherein the location search information comprises relative positionsthat define the ad-hoc boundary region.
 17. A non-transitorycomputer-readable storage medium comprising instructions stored thereon,which when executed by one or more processors, cause the one or moreprocessors to perform operations comprising: receiving a query input,wherein the query input defines a field search query for raw machinedata; receiving a map input at an interactive map portion, wherein theinteractive map portion displays a map region; generating a display of auser-defined overlay of the map region based on the map input, whereinthe user-defined overlay depicts one or more ad-hoc boundary regions;generating location search information for searching raw machine databased on the one or more ad-hoc boundary regions; generating a combinedquery based on the field search query and the location searchinformation; transmitting the combined query to a data query system;receiving a set of results from the data query system, wherein thereceived results are responsive to both the field search query and thelocation search information, and wherein individual results within theset of results identify a value from the raw machine data and atimestamp corresponding to the value; displaying a visualization of theset of results as an ordering of the set of results over a time range,wherein the ordering is determined based at least in part on thetimestamps identified within the results, and wherein a portion of thevisualization is selectable to indicate a subrange of the time range;obtaining a selection of the portion of the visualization; anddisplaying a subset, of the set of results, that identify a timestampwithin the subrange.
 18. The non-transitory computer-readable storagemedium of claim 17, wherein the operations further comprise: receivingone or more location identifiers from the data query system, whereineach location identifier corresponds to the location search informationand is associated with a lookup definition for the one or more ad-hocboundary regions; and storing the location identifiers; displaying aselection interface for the location identifiers; receiving a selectionof one or more of the location identifiers; updating the user-definedoverlay of the map region based on the selection; receiving a second mapinput at the interactive map portion; and identifying a new ad-hocboundary region based on the updated user-defined overlay and the secondmap input.
 19. The non-transitory computer-readable storage medium ofclaim 17, wherein the operations further comprise: receiving one or morelocation identifiers from the data query system, wherein each locationidentifier corresponds to the location search information and isassociated with a lookup definition for the one or more ad-hoc boundaryregions; and storing the location identifiers.
 20. The non-transitorycomputer-readable storage medium of claim 17, wherein the operationsfurther comprise: receiving one or more location identifiers from thedata query system, wherein each location identifier corresponds to thelocation search information and is associated with a lookup definitionfor the one or more ad-hoc boundary regions; and storing the locationidentifiers; accessing a plurality of previously stored locationidentifiers, displaying a selection interface for the received locationidentifiers and the preexisting location identifiers; receiving aselection of one or more of the received location identifiers and one ormore of the preexisting location identifiers; updating the user-definedoverlay of the map region based on the selection; and identifying a newad-hoc boundary region based on the updated user-defined overlay. 21.The non-transitory computer-readable storage medium of claim 17, whereinthe operations further comprise: identifying based on the timestamps, asubset of the values that correspond to real-time data; and displayingthe subset of the values that correspond to real-time data.
 22. Thenon-transitory computer-readable storage medium of claim 17, wherein theoperations further comprise: identifying, based on the timestamps,whether a threshold number of the values correspond to real-time data;and generating a notification when the threshold number of the valuescorresponds to real-time data.
 23. A system for providing a userinterface for an interactive data query, the system comprising: a userinterface; at least one memory having instructions stored thereon; andat least one processor configured to execute the instructions, whereinthe at least one processor is configured to: receive a query input fromthe user interface, wherein the query input defines a field search queryfor raw machine data; receive a map input at an interactive map portionof the user interface, wherein the interactive map region portion a mapregion; generate a display of a user-defined overlay of the map regionbased on the map input, wherein the user-defined overlay depicts one ormore ad-hoc boundary regions at the user interface; generate locationsearch information for searching raw machine data based on the one ormore ad-hoc boundary regions; generate a combined query based on thefield search query and the location search information; transmit thecombined query to a data query system; receive a set of results from thedata query system, wherein the received results are responsive to boththe field search query and the location search information, and whereinindividual results within the set of results identify a value from theraw machine data and a timestamp corresponding to the value; display avisualization of the set of results as an ordering of the set of resultsover a time range, wherein the ordering is determined based at least inpart on the timestamps identified within the results, and wherein aportion of the visualization is selectable to indicate a subrange of thetime range; obtain a selection of the portion of the visualization; anddisplay a subset, of the set of results, that identify a timestampwithin the subrange.
 24. The system of claim 23, wherein the at leastone processor is further configured to: receive one or more locationidentifiers from the data query system, wherein each location identifiercorresponds to the location search information and is associated with alookup definition for the one or more ad-hoc boundary regions; and storethe location identifiers; display a selection interface for the locationidentifiers; receive a selection of one or more of the locationidentifiers; update the user-defined overlay of the map region based onthe selection; receive a second map input at the interactive mapportion; and identify a new ad-hoc boundary region based on the updateduser-defined overlay and the second map input.
 25. The system of claim23, wherein the at least one processor is further configured to: receiveone or more location identifiers from the data query system, whereineach location identifier corresponds to the location search informationand is associated with a lookup definition for the one or more ad-hocboundary regions; and store the location identifiers.
 26. The system ofclaim 23, wherein the at least one processor is further configured to:receive one or more location identifiers from the data query system,wherein each location identifier corresponds to the location searchinformation and is associated with a lookup definition for the one ormore ad-hoc boundary regions; and store the location identifiers; accessa plurality of previously stored location identifiers, display aselection interface for the received location identifiers and thepreexisting location identifiers; receive a selection of one or more ofthe received location identifiers and one or more of the preexistinglocation identifiers; update the user-defined overlay of the map regionbased on the selection; and identify a new ad-hoc boundary region basedon the updated user-defined overlay.
 27. The system of claim 23, whereinthe at least one processor is further configured to update a resultsdisplay based on the received results.
 28. The system of claim 23,wherein the at least one processor is further configured to: identifybased on the timestamps, a subset of the values that correspond toreal-time data; and display the subset of the values that correspond toreal-time data.
 29. The system of claim 23, wherein the at least oneprocessor is further configured to: identify, based on the timestamps,whether a threshold number of the values correspond to real-time data;and generate a notification when the threshold number of the valuescorresponds to real-time data.